The vast datacentres required are so expensive that many are financed by debt secured against future revenue. The vast datacentres required are so expensive that many are financed by debt secured against future revenue. Sun 4 Jan 2026 07.18 ESTLast modified on Sun 4 Jan 2026 08.47 EST The US dictionary Merriam-Websterâ s word of the year for 2025 was â slopâ, which it defines as â digital content of low quality that is produced, usually in quantity, by means of artificial intelligenceâ . The choice underlined the fact that while AI is being widely embraced, not least by corporate bosses keen to cut payroll costs, its downsides are also becoming obvious. In 2026, a reckoning with reality for AI represents a growing economic risk.

Daniel Fallmann is Founder and CEO of Mindbreeze, a leader in enterprise search, applied artificial intelligence and knowledge management. Artificial intelligence (AI) can make a great addition to your toolkit. But the truth is that AI can also be a waste of time and money if you use it to try to solve the wrong problems or don't have clear objectives from the beginning. Understand What AI Can and Can't Dos AI becomes increasingly commonplace, it's important to understand exactly what it can and can't do. No matter what you may have heard, AI isn't going to solve all your problems.

Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers. Banks, telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics (along with cash flow, EBITDA, etc.) because the cost of retaining an existing customer is far less than acquiring a new one. Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients. Churn prediction is one of the most popular Big Data use cases in business. It consists of detecting customers who are likely to cancel a subscription to a service.

Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers. Banks, telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics (along with cash flow, EBITDA, etc.) because the cost of retaining an existing customer is far less than acquiring a new one. Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients. Churn prediction is one of the most popular Big Data use cases in business. It consists of detecting customers who are likely to cancel a subscription to a service.